There are many problems where two signals are correlated, but have a distinct time delay between them. One example is a flow sensor consisting of two individual sensor channels that are spatially separated and positioned perpendicular to the airflow. In this sensor, the signals produced by the two individual sensor channels are correlated, meaning that the spatial distance between them is small enough that perturbations in the first signal are statistically present in the second signal, but there is a time delay between the two signals. This time delay is proportional to the air flow speed over the sensor.
Calculation of this time delay is traditionally accomplished by performing a correlation function, which results in the final output of the flow speed. This correlation is conventionally achieved using an analog-to-digital converter sampling system and digital signal processor optimized to perform the calculations required of the correlation function. This function requires many multiplications and additions, which is very processing heavy and is sometimes not feasible at the desired sampling rate.
Often, and perhaps most importantly, the requisite processing hardware for conventional analog correlation is relatively advanced, and has significant mass and volume. Such processing hardware also takes significant time to process the signals and has significant power requirements, sometimes on the order of 2-3 Watts or more. These constraints have precluded the use of analog correlation functions in many target applications. Accordingly, analog correlation hardware that is small, low power, and fast may be beneficial. It may also be beneficial for such analog correlation to be optimized for field programmable gate arrays (FPGAs) that are often represented by radiation hardened and high temperature technologies. Such optimization opens up the opportunity for sensors requiring time domain correlation to be embedded into critical and harsh environments.